The analysis below is for Baltimore homeownership incentive programs including:
Weatherization and repairs will be added once geocoding is completed.
The primary research questions are:
Loading required package: spEach incentive distribution was geocoded to repair/replace neighborhood assignment and to provide point locations for any later clustering or hot spot analysis.
The summary table below gives the percent of program funding that was distributed to each housing market type. Live Near Your Work is utilized primarily in strong/healhty markets, while 85% of CDBG funding goes to middle neighborhoods.
incen %>%
group_by(prog.type, hmt.group) %>%
summarise(total = sum(as.numeric(amount))) %>%
mutate(total = ifelse(is.na(total), 0, total)) %>%
filter(!is.na(prog.type)) %>%
ungroup() %>%
group_by(prog.type) %>%
mutate(pct = percent(total / sum(total))) %>%
select(-total) %>%
spread(key = prog.type, value = pct)The map and table below present a program “utilization” metric - the number of incentives used per 100 home sales.
The summary table below gives program “utilization” by housing market typology tier.